A two-stage approach for measuring vascular water exchange and arterial transit time by diffusion-weighted perfusion MRI

Keith S St Lawrence, Daron Owen, Danny J J Wang, Keith S St Lawrence, Daron Owen, Danny J J Wang

Abstract

Changes in the exchange rate of water across the blood-brain barrier, denoted k(w), may indicate blood-brain barrier dysfunction before the leakage of large-molecule contrast agents is observable. A previously proposed approach for measuring k(w) is to use diffusion-weighted arterial spin labeling to measure the vascular and tissue fractions of labeled water, because the vascular-to-tissue ratio is related to k(w). However, the accuracy of diffusion-weighted arterial spin labeling is affected by arterial blood contributions and the arterial transit time (τ(a)). To address these issues, a two-stage method is proposed that uses combinations of diffusion-weighted gradient strengths and post-labeling delays to measure both τ(a) and k(w). The feasibility of this method was assessed by acquiring diffusion-weighted arterial spin labeling data from seven healthy volunteers. Repeat measurements and Monte Carlo simulations were conducted to determine the precision and accuracy of the k(w) estimates. Average grey and white matter k(w) values were 110 ± 18 and 126 ± 18 min(-1), respectively, which compare favorably to blood-brain barrier permeability measurements obtained with positron emission tomography. The intrasubject coefficient of variation was 26% ± 23% in grey matter and 21% ± 17% in white matter, indicating that reproducible k(w) measurements can be obtained.

Copyright © 2011 Wiley Periodicals, Inc.

Figures

Fig. 1
Fig. 1
Illustrated example of the impulse residue function (q(t), solid line) and its capillary (qc(t), dashed line) and tissue (qt(t), dotted line) components as defined by Eqn. (2). In this example, CBF = 50 ml/100g/min, PS = 150 ml/100g/min, Vc = 2.0 ml/100g, T1a = 1.5 s, and T1b = 1.26 s.
Fig. 2
Fig. 2
Predicted capillary fraction of labeled water (A1) plotted as a function of the water-exchange rate (kw). Simulated data were generated using T1b = 1.26 s, T1c = 1.5 s, a labeling duration of 1.5 s, τa = 1.26 s and a post-labeling delay of 1.5 s.
Fig. 3
Fig. 3
DW-ASL sequence incorporated pseudo-continuous ASL (pCASL), background suppression (BS), and twice-refocused spin-echo diffusion weighting (TRSE).
Fig. 4
Fig. 4
Average ΔM images acquired under the multiple b value protocol from one individual with the delay of 1200 ms. Images from 5 b values are presented to illustrate the general effect of varying the diffusion weighting.
Fig. 5
Fig. 5
Average ΔM signal (symbols) acquired at four post-labeling delays plotted as a function of diffusion-weighting strength. Data were averaged across all pixels in grey matter (A) or white matter (B) and across 4 subjects. Each subject's data were normalized to the signal without diffusion weighting (b = 0). The errors bars represent the standard error. The solid lines represent the best fit of a bi-exponential decay model (Eq. (6)) to each ΔM series.
Fig. 6
Fig. 6
DW-ASL ΔM images from one subject: (A) FEAST data acquired with τd = 0.8 s and (B) DW-ASL(kw) data acquired with τd = 1.5 s. Each set shows the average diffusion-weighted ΔM images, ΔM (b0) in the first row and ΔM (bDW) in the second row; the ratio images ΔM(bDW)/ΔM(b0) are in the third row; and either the (A) τa or (B) kw images are in the final row (the τa scale is in units of s and the kw scale in min−1). Four of 8 slices are shown. All images were smoothed with a Gaussian filter with a kernel size of 8 mm for the FEAST images and 15 mm for the DW-ASL(kw) images. Images were generated using T1 values of 1.26, 0.85 and 1.49 s for grey matter, white matter and blood, respectively.
Fig. 7
Fig. 7
(A) Histogram of ΔM(b50/ΔM(b0) values across all pixels from one subject's data. The ordinate represents the relative number of pixels at a given ΔM(b50/ΔM(b0) value. (B) Histogram of corresponding kw values.
Fig. 8
Fig. 8
Error in water-exchange rate kw due to random noise added to the capillary fraction (A1) signal. The dotted black line represents the input kw value and the solid black line represents the mean value from the simulations. The two dashed red lines are the 16 and 84 percentiles, and the two solid red lines are the 5 and 95 percentiles.

Source: PubMed

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